CN116966056A

CN116966056A - Upper limb rehabilitation evaluation system for cerebral apoplexy patient

Info

Publication number: CN116966056A
Application number: CN202310978929.0A
Authority: CN
Inventors: 吕晓东; 刘海杰; 陈腾; 姚辉; 李美洁; 韩博
Original assignee: Xuanwu Hospital
Current assignee: Xuanwu Hospital
Priority date: 2023-08-04
Filing date: 2023-08-04
Publication date: 2023-10-31

Abstract

The invention relates to an upper limb rehabilitation evaluation system of a cerebral apoplexy patient, which at least comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring upper limb information of the cerebral apoplexy patient; the processing module is used for processing the upper limb information acquired by the acquisition module; the training module is used for assisting a cerebral apoplexy patient to perform upper limb movement; the display module is used for displaying the upper limb movement state of the cerebral apoplexy patient; the display module displays the difference between the virtual upper limb and the target state to a cerebral apoplexy patient so that the cerebral apoplexy patient drives the real upper limb to move; the processing module judges the movement capacity of the real upper limb based on the difference change between the virtual upper limb and the target state, and controls the training module to perform auxiliary movement according to the movement capacity, wherein the processing module adjusts the driving moment applied to the bilateral upper limb by the training module based on the electromyographic signal difference between the bilateral upper limb acquired by the acquisition module. The processing module evaluates the exercise capacity and coordination of the bilateral upper limbs and gives out targeted rehabilitation assistance.

Description

Upper limb rehabilitation evaluation system for cerebral apoplexy patient

Technical Field

The invention relates to the technical field of cerebral apoplexy rehabilitation, in particular to an upper limb rehabilitation evaluation system for cerebral apoplexy patients.

Background

Among cerebral apoplexy patients, hemiplegia patients with paralysis of a part of left and right limbs exist, and systems for helping these patients to perform rehabilitation training are becoming popular. The existing rehabilitation training for paralysis of the upper limb at least comprises auxiliary exercise rehabilitation training by means of an upper limb rehabilitation robot, an exoskeleton and the like, the rehabilitation robot can provide high-strength, repeated, task-specific and interactive treatment for the upper limb of paralysis, and simultaneously provides an evaluation method for objective exercise function recovery, and changes of kinematics and exercise force are measured, however, the upper limb rehabilitation robot cannot improve the muscle strength of the upper limb. The motor function evaluation is the basis for a doctor to make a rehabilitation training plan for a cerebral apoplexy hemiplegia patient, can be used as an evaluation means of the curative effects of various treatment schemes, and is vital in the fields of motor rehabilitation and neuroscience. Traditional exercise rehabilitation assessments are based primarily on scales, performed by specialized therapists. The gauge evaluation method is early in development and has proved to be feasible clinically, but has three disadvantages: the evaluation result is greatly influenced by the subjective of therapists, different therapists evaluate the evaluation result by experience, different therapists possibly give different evaluation results for the same patient according to different standards; the scale type evaluation method gives corresponding scores according to different performances of corresponding actions of the patient, the score gradient is small, the evaluation result cannot reflect the real situation of the patient, and in addition, some fine movement therapists of the patient can be difficult to capture visually, so that the evaluation result is incomplete; the existing scale type evaluation method has the defects that a therapist guides a patient to execute a series of actions, one patient is at least matched with one therapist, and the evaluation efficiency is low; meanwhile, a set of evaluation flow is time-consuming and labor-consuming.

A method and apparatus for evaluating exercise functions based on joint movement degree and coordination of exercise proposed in the prior art as publication No. CN110755085B, the method comprising: acquiring limb movement signals of a target object; and extracting joint activity data and motion coordination data in the limb motion signals, and evaluating the motion function of the limb, wherein the joint activity data comprises active motion data and passive motion data. The training standard of the existing rehabilitation training evaluation system is usually limb reflection values of normal people. However, for cerebral apoplexy patients, the limb training response condition cannot reach the normal standard in a short time. Especially, most cerebral apoplexy patients have left and right side offset paralysis, which leads to a relatively obvious difference in training response conditions of left and right limbs. If the general standard is directly adopted to train and evaluate the patient, the same training intensity is provided for the left and right limbs of the patient, so that the limb with poor response bears excessive training pressure, the rehabilitation purpose cannot be achieved, and larger damage is possibly caused. Meanwhile, the non-differential training slows down the rehabilitation process of the patient, and causes a blocking factor for the normal life of the patient.

Furthermore, there are differences in one aspect due to understanding to those skilled in the art; on the other hand, as the inventors studied numerous documents and patents while the present application was made, the text is not limited to details and contents of all that are listed, but it is by no means the present application does not have these prior art features, the present application has all the prior art features, and the applicant remains in the background art to which the rights of the related prior art are added.

Disclosure of Invention

Aiming at the defects of the technical proposal provided by the prior art, the application provides an upper limb rehabilitation evaluation system for a cerebral apoplexy patient, which at least comprises: the acquisition module is used for acquiring upper limb information of a cerebral apoplexy patient; the processing module is used for processing the upper limb information acquired by the acquisition module; the training module is used for assisting a cerebral apoplexy patient to perform upper limb movement; the display module is used for displaying the upper limb movement state of the cerebral apoplexy patient; the display module displays the difference between the virtual upper limb and the target state to a cerebral apoplexy patient so that the cerebral apoplexy patient drives the real upper limb to move; the processing module judges the movement capacity of the real upper limb based on the difference change between the virtual upper limb and the target state, and controls the training module to perform auxiliary movement according to the movement capacity, wherein the processing module adjusts the driving moment applied to the bilateral upper limb by the training module based on the electromyographic signal difference between the bilateral upper limb acquired by the acquisition module. The processing module evaluates the exercise capacity and coordination of the bilateral upper limbs and gives out targeted rehabilitation assistance.

In the above scheme, the virtual upper limb refers to an upper limb model constructed in the display module, the real upper limb refers to an upper limb of a patient in an actual environment, the target state refers to an upper limb pattern which is randomly generated in the display module and is expected to enable the patient to move the upper limb to a target position and a target posture, and the difference between the virtual upper limb and the target state refers to the difference between the current position and the target position of the virtual upper limb and the difference between the current posture and the target posture of the virtual upper limb.

The application focuses on the limb movement difference of the left and right sides of the patient, provides different and targeted training parameters for the two sides of the patient, improves the training effect and training efficiency, simultaneously avoids the condition that the left and right limb muscles are uncoordinated in the rehabilitation process due to the application of inappropriate training intensity, solves the problem that the normal life of the patient is blocked due to the uncoordinated actions of the two sides of the limb, and has a certain positive influence on the life quality of the cerebral apoplexy patient.

Preferably, the processing module controls the driving moment of the training module to be enhanced in a gradual and slow mode, the training module stops enhancing when the real upper limb has a movement trend, and the real upper limb is driven in an auxiliary mode by the margin of the current driving moment plus the preset proportion of the current driving moment. Under this kind of mode, the driving force of actual motion of true upper limbs still drives for the auxiliary mode with the main drive of cerebral apoplexy patient independently, and training module drive, and the surplus value of increase can avoid the muscle to excessively use, protects cerebral apoplexy patient's muscle health.

Preferably, the processing module adjusts the magnitude of the residual value in a manner of adjusting the preset proportion based on the difference value between the first electromyographic signals and the second electromyographic signals of the upper limbs on the two sides respectively acquired by the acquisition module, and further adjusts the total value of the driving torque applied to the upper limb on one side. The first electromyographic signal and the second electromyographic signal refer to electromyographic signals under the condition of movement of one upper limb of a patient acquired by an electromyographic acquisition part of an acquisition module, specifically, the first electromyographic signal refers to electromyographic signals generated by movement of the left upper limb, and the second electromyographic signal refers to electromyographic signals generated by the right upper limb. The total value of the drive torque is equal to the drive torque at which the tendency to move occurs and is increased by a further margin according to a predetermined proportion (for example, one tenth) of the drive torque.

Assuming that the intensity of the first electromyographic signal of the left upper limb is greater than the intensity of the second electromyographic signal of the right upper limb, namely, the recovery degree of the left upper limb is better than that of the right upper limb, therefore, when the training module applies the driving moment, the driving moment applied to the right upper limb is smaller than that of the left upper limb, the design logic of the mode is that the right upper limb with poorer recovery degree is applied with smaller driving moment, so that the right upper limb is subjected to more exercise, and due to the smaller driving moment, the attention of a cerebral apoplexy patient is concentrated to drive the right upper limb to move in the movement process of the right upper limb, and the recovery effect of the right upper limb is gradually leveled with the left upper limb, so that the coordinated effect of the bilateral upper limb is kept in the rehabilitation training process of the bilateral upper limb is realized.

Preferably, the processing module is configured to construct a virtual upper limb in the virtual environment of the display module based on at least the myoelectric signal and the motion information of the real upper limb acquired by the acquisition module, where the acquisition module includes at least a myoelectric acquisition portion for acquiring the myoelectric signal and a motion capture portion for capturing motion.

Preferably, the step of mapping the real upper limb to the virtual upper limb of the virtual environment by the processing module comprises:

building a model of a virtual upper limb in a virtual environment;

collecting electromyographic signals and posture information of a real upper limb;

extracting the electromyographic signals, the motion information characteristics and the posture information of the real upper limb, and classifying the electromyographic signals, the motion information characteristics and the posture information of the real upper limb in a stepwise manner according to the strength of the characteristics;

the myoelectricity characteristics and the motion angle characteristics are fused and then input into a classifier for pattern recognition;

dynamically recognizing the action and the movement of the real upper limb, and synchronously adjusting the action and the movement of the virtual upper limb.

Preferably, the display module is configured in a manner that the display module can be observed by a cerebral apoplexy patient, so that the cerebral apoplexy patient continuously looks like a situation that the virtual upper limb moves to a target state when observing the picture of the virtual upper limb and the target state in the display module.

Preferably, the processing module can analyze the time and the movement state of the virtual upper limb moving to the target state in the display module, and judge the movement capacity of the real upper limb in the actual environment according to the time and the movement state.

Preferably, the processing module can analyze the driving angle and driving moment required by the training module for auxiliary movement based on inverse kinematics, wherein the driving moment calculated by the processing module is the maximum driving moment which can be provided by the training module under the current auxiliary movement.

Preferably, the step of calculating the driving angle and the driving moment by the processing module includes:

obtaining the current position of the tail end of the virtual upper limb and the target position of the target state, and calculating the driving angles of all joints from the current position and the target position based on an inverse kinematics algorithm;

based on iterative learning, dynamically adjusting arm supporting moment of each joint, and calculating to obtain arm supporting moment required by each joint according to the target driving angle, the actual joint angle corresponding to the target driving angle, the target joint angular velocity and the actual joint angular velocity;

performing inverse dynamics control based on feedback linearization, calculating to obtain output moment of each joint according to target motion parameters and actual motion parameters of each joint, and calculating to obtain driving quantity of each joint according to the output moment of each joint;

And controlling the joint to be trained to apply driving moment according to the driving quantity of each joint so as to perform rehabilitation training.

Preferably, the myoelectricity collecting part of the collecting module and the training module are jointly configured at the upper limbs on two sides of the cerebral apoplexy patient, and the motion capturing part of the collecting module and the display module are jointly configured.

According to a preferred embodiment, the application provides an upper limb rehabilitation evaluation system for a cerebral apoplexy patient, which can be applied to upper limb rehabilitation effect evaluation of a cerebral apoplexy patient in individual families. In the existing rehabilitation effect evaluation technical scheme of the cerebral apoplexy patient, the patient is usually required to carry out detection of various instruments and various exercise analyses to a specific professional medical staff to obtain the rehabilitation condition of the patient, and the mode is a serious time burden and economic burden for a common family, so that the application provides a portable household rehabilitation evaluation system for the upper limb of the cerebral apoplexy patient, each exercise of the upper limb can be displayed through a display module, then the patient carries out the same exercise according to the displayed exercise imagination of the upper limb of the patient, then the electromyographic signals generated by the exercise of the upper limb are analyzed and compared, the rehabilitation effect can be evaluated, and the coordination between the upper limbs in the rehabilitation process can be evaluated, so that the rehabilitation evaluation system is arranged in the individual family, the cerebral apoplexy patient can evaluate the rehabilitation condition of the cerebral apoplexy patient at any time through the system, and the specific rehabilitation training can be carried out.

Drawings

FIG. 1 is a simplified relational structure diagram of the upper limb rehabilitation evaluation system for stroke patients of the present application;

FIG. 2 is a simplified schematic diagram of the display module of the upper limb rehabilitation evaluation system for stroke patients according to the present application;

fig. 3 is a simplified structural diagram of a training module of the upper limb rehabilitation evaluation system for a cerebral apoplexy patient according to the present application.

List of reference numerals

100: an acquisition module; 200: a processing module; 300: a training module; 400: a display module; 110: a myoelectricity acquisition unit; 120: a motion capturing part.

Detailed Description

The present application will be described in detail with reference to fig. 1 to 3.

Fig. 1 is a simplified relational structure diagram of an upper limb rehabilitation evaluation system for a cerebral apoplexy patient according to the present application, which at least includes a training module 300 for applying auxiliary motions, an acquisition module 100 for acquiring physiological information of bilateral upper limbs and motion states, a display module 400 for displaying target states, and a processing module 200 for analyzing and processing. The acquisition module 100 can at least acquire the motion state of the bilateral upper limbs and the electromyographic signals after the motion, the processing module 200 virtualizes the upper limbs of the patient to the display module 400 based on the motion state so as to observe the cerebral apoplexy patient, and the target state is displayed in the display module 400, so that the cerebral apoplexy patient needs to move the actual bilateral upper limbs to the target state according to the observed target state with the aid of the training module 300.

The technical scheme of using the display module 400 to enable the motion state of bilateral upper limbs to be observed by a patient suffering from cerebral apoplexy is extension of a motor imagery therapy, which refers to repeated motor imagery for improving a motor function without any motion output, activating a specific region of a certain activity in the brain according to a motion memory, more activating the frontal lobe front part and the parietal lobe rear part than an actual motion, and a mirror therapy, which refers to placing a mirror in a direction of the patient for easy observation, placing a non-paralyzed side upper limb in front of the mirror, and placing an affected side upper limb behind the mirror. The upper limb on the non-paralyzed side carries out buckling and stretching movements of the wrist and the finger, a patient looks at a mirror at the same time, views mirror images of the upper limb on the non-paralyzed side, the upper limb on the paralyzed side carries out the same movements of the upper limb on the non-paralyzed side, and the mirror image therapy can directly promote the recovery of the motor function by adjusting the excitability of cortex-muscle, and belongs to one of the motor imagination therapies and is based on repeated motor function imagination.

In the present application, according to the simplified structure diagram of the display module 400 of the upper limb rehabilitation evaluation system for the cerebral apoplexy patient shown in fig. 2, the upper limb movement on the non-paralyzed side for comparison adopted by the mirror therapy is set as the target state, that is, the display module 400 is equivalent to the mirror surface in the mirror therapy, the target state in the display module 400 is the upper limb on the non-paralyzed side in the mirror therapy, and the cerebral apoplexy patient can imagine how the actual bilateral upper limb should move in the brain to enable the simulated virtual upper limb in the display module 400 to move from the current position to the target state position by observing the movement state of the upper limb simulated in the display module 400, thereby promoting the excitement and the relativity between the cerebral cortex and the upper limb muscles, and assisting the cerebral apoplexy upper limb paralyzed patient to perform rehabilitation training.

Preferably, the acquisition module 100 can acquire the upper limb movement state information in the actual environment and display the upper limb movement state information in the display module 400 in the form of a virtual upper limb after the upper limb movement state information is processed and analyzed by the processing module 200, and the virtual upper limb in the display module 400 can synchronously adjust the state of the virtual upper limb based on the actual upper limb movement state information acquired by the acquisition module 100 in real time.

Preferably, the processing module 200 can accurately analyze the motion trail of the virtual upper limb in the display module 400, specifically, the processing module 200 can obtain what kind of trail of motion needs to be performed when the current position of the virtual upper limb moves to the target state position, and further obtain auxiliary motion parameters of the training module 300 for assisting the upper limb to move in the actual environment, so that the upper limb can complete the target state.

Preferably, the target state in the display module 400 may be static or dynamic, where the static target state refers to a specific gesture, such as arm extension, crank arm, fist making, etc., and the dynamic target state refers to an action that the arm can perform, for example:

finger stretching: is completed by the synergic action of the metacarpophalangeal muscle, the extensor digitorum longus and the extensor digitorum abductor of thumb;

finger buckling: is completed by the synergistic action of flexor digitorum, thumb flexor and wrist flexor;

Fist making: is completed by the muscle actions of metacarpophalangeal muscles, extensor digitorum longus, extensor thumb and the like;

opening the fingers: is completed by the muscle actions of metacarpal extensor, extensor digitorum and extensor thumb;

rotating the palm: is completed by the synergism of forearm rotary muscle, flexor carpi and extensor carpi;

lifting the wrist: is completed by the muscle actions of flexor carpi radialis extensor carpi radialis and extensor ulnar carpi radialis;

the myoelectric signals of the real upper limb with the action and the gesture as target states are respectively acquired by the myoelectric sensors arranged at the corresponding muscle parts.

Preferably, the acquisition module 100 includes a myoelectricity acquisition portion 110 and a motion capture portion 120, wherein the myoelectricity acquisition portion 110 is configured to acquire information such as myoelectric signals in different motion states and send the information to the processing module 200, and the processing module 200 constructs a muscle change condition of a virtual upper limb in the display module 400 based on the information such as the myoelectric signals acquired by the myoelectricity acquisition portion 110; the motion capture unit 120 is configured to obtain joint coordinates and spatial posture information of the upper limb, and send the information to the processing module 200, and the processing module 200 adjusts coordinates and posture of the virtual upper limb on the display module 400 based on the joint coordinates and spatial posture information.

Preferably, the myoelectricity acquisition part 110 adopts a surface myoelectricity sensor, a myoelectricity electrode and/or a myoelectricity arm ring, the myoelectricity acquisition part 110 can be used for acquiring information such as myoelectricity signals when two side upper limbs in an actual environment perform different motion states, and the portable myoelectricity arm ring can also acquire displacement signals of upper limbs of a cerebral apoplexy patient, specifically, the myoelectricity arm ring can be respectively worn on the forearm and the upper arm of the cerebral apoplexy patient, and can be respectively used for acquiring bioelectricity signals generated by muscles of the forearm and the upper arm, motion acceleration generated by motion and displacement signals.

Preferably, the display module 400 essentially provides a virtual environment, wherein the virtual upper limbs and the target state positions are located inside the virtual environment of the display module 400, and the virtual environment of the display module 400 is built by using the prior art, such as Unity3D or other software technology capable of three-dimensional modeling, which is widely applied to the field of virtual modeling, so that the disclosure is not repeated herein.

Preferably, the processing module 200 maps the upper limb information acquired by the acquisition module 100 onto the virtual upper limb of the display module 400, specifically including mapping for shoulder, elbow and finger joints in the upper limb.

Preferably, in the present application, the processing module 200 constructs a virtual upper limb capable of synchronizing the upper limb movement condition in the actual environment in the virtual environment of the display module 400 based on the upper limb movement information and the posture information acquired by the myoelectricity acquisition part 110 and the motion capture part 120 of the acquisition module 100, and specifically includes the following steps:

s110: building a model of a virtual upper limb in a virtual environment; mapping the real upper limb in the actual environment into a virtual upper limb in the virtual environment of the display module 400, and mapping the motion information of the real upper limb acquired by the myoelectricity acquisition part 110 and the motion capture part 120 into the virtual upper limb of the virtual environment;

S120: collecting electromyographic signals and posture information of a real upper limb; the myoelectric signals and the posture information of the upper limb are acquired by the myoelectric acquisition part 110 and the motion capture part 120;

s130: extracting the electromyographic signals, the motion information characteristics and the posture information of the real upper limb, and classifying the electromyographic signals, the motion information characteristics and the posture information of the real upper limb in a stepwise manner according to the strength of the characteristics;

s140: the myoelectricity characteristics and the motion angle characteristics are fused and then input into a classifier for pattern recognition;

s150: dynamically recognizing the action and the movement of the real upper limb, and synchronously adjusting the action and the movement of the virtual upper limb; communication between the virtual environment and mathematical software such as MATLAB is established, the movements and motions of the real upper limb are recognized online, and the movements and motions of the virtual upper limb in the display module 400 are adjusted synchronously.

Preferably, in step S110, the mapping of the virtual upper limb includes mapping the hand and forearm of the real upper limb captured by the motion capturing unit 120, and the motion information of the upper limb into the hand and forearm of the virtual upper limb of the virtual environment, assigning the preset of the myoelectric arm ring to the upper limb of the virtual environment, synchronizing the motion information of the upper limb, assigning the gesture position information of each joint and wrist joint of the finger captured by the hand image motion capturing unit 120 to the finger and forearm of the virtual upper limb, synchronizing the motion information of the finger and forearm, calibrating the initial directions of the upper limb and forearm in the virtual environment, and adjusting the virtual upper limb to synchronize the real upper limb.

Preferably, in step S120, the acquisition module 100 includes myoelectric signals acquired by the myoelectric acquisition unit 110 or the myoelectric electrode, the motion capture unit 120 of the acquisition module 100 is arranged at the display module 400 to acquire gesture information and gesture information of the forearm and the upper arm by the motion capture unit 120, the portable myoelectric arm ring communicates with the processing module 200 through the bluetooth receiver, the processing module 200 acquires the forearm myoelectric information when executing different gestures by setting a manner of acquiring myoelectric in a virtual environment, and various gesture actions of a gesture library are established, the acquisition manner includes a process of preparing a gesture action countdown-executing a gesture action-repeating action, gesture position information of the finger and the forearm captured by the hand image motion capture unit 120 is calculated to obtain finger bending angle, elbow joint activity and the like, and upper arm movement information acquired by the portable myoelectric arm ring is calculated to obtain shoulder joint activity.

Preferably, in step S130, the feature extraction includes extracting time domain features of the electromyographic signals, including average absolute values, zero crossing points and coefficients of an autoregressive model, extracting gesture information features such as joint activity, extracting gesture information of the upper limb such as position information, setting a spatial motion track, and calculating a track deviation degree; and taking myoelectric characteristics and gesture information of a normal person as templates, carrying out step classification according to the characteristic intensity, and acquiring corresponding scores according to the corresponding step classification by using the myoelectric characteristics and gesture information of the user.

Preferably, in step S140, pattern recognition includes fusing myoelectric characteristics and angle characteristics of gestures, inputting a classifier such as a BP neural network to perform pattern classification, storing parameters and threshold values of virtual upper limbs, and determining a movement direction using posture information such as position information as a condition.

Preferably, in step S150, the online identification is specifically: invoking a function of MATLAB in the processing module 200 or establishing communication between the virtual environment in the display module 400 and MATLAB, and then identifying and classifying the stroke patient's actions in the processing module 200, and driving the virtual upper limbs to perform synchronous movements in the virtual environment according to the classification.

Preferably, the processing module 200 can analyze the time of the movement of the virtual upper limb in the display module 400 to the target state, determine the movement capability of the real upper limb in the actual environment according to the time and the movement state, and give a certain amount of assistance and support through the training module 300 under the condition of insufficient movement capability, so as to help the stroke patient complete the movement from the initial state to the target state.

Example 2

This embodiment is based on the improvement and supplement of embodiment 1, and the repeated contents will not be repeated.

As shown in fig. 1, the upper limb rehabilitation evaluation system for a patient suffering from cerebral apoplexy at least comprises an acquisition module 100, a processing module 200, a training module 300 and a display module 400, wherein the display module 400 is configured in a manner that the upper limb rehabilitation evaluation system can be observed by the patient suffering from cerebral apoplexy and displays a target state of upper limb movement and a virtual upper limb in the display module 400, wherein the virtual upper limb is mapped into a virtual environment of the display module 400 by the processing module 200 based on upper limb information acquired by the acquisition module 100, and in the training process, the processing module 200 can judge the movement capacity of a real upper limb in an actual environment according to the movement time and the movement state of the virtual upper limb in the virtual environment of the display module 400, and when the processing module 200 considers that the movement capacity of the real upper limb is insufficient to complete the movement from an initial state to the target state, the processing module 200 sends a control signal to the training module 300, and the training module 300 assists the real upper limb to complete the movement in the actual environment.

Preferably, the processing module 200 mainly determines according to the difference between the virtual upper limb and the target state in the virtual environment of the display module 400, for example, the virtual upper limb in the display module 400 does not move to the preset position of the target state within a certain preset period of time, or the gesture is not accurate enough after moving to the preset position, specifically, the processing module 200 can rank the movement capability of the virtual upper limb (the virtual upper limb is synchronous with the real upper limb, essentially refers to the real upper limb) according to the difference between the virtual upper limb and the target state, and in more detail, the processing module 200 can also refer to the myoelectric signal of the real upper limb acquired by the myoelectric acquisition part 110 of the acquisition module 100 in the ranking process, and determine the movement capability of the real upper limb to a certain extent by analyzing the myoelectric signal.

Preferably, when the processing module 200 considers that the motion ability of the real upper limb (virtual upper limb) is insufficient, the processing module 200 analyzes the driving angle and driving moment that the training module 300 needs to provide based on the inverse kinematics.

Preferably, for the movement state of the upper limb, it can substantially disassemble various movements of the whole upper limb into rotational movements of the joints, specifically, no matter what state or what form of normal movement is performed on the upper limb, the length and shape of bones of the upper limb are not changed, and the flexibility of the upper limb is mainly determined by the multi-angle flexible rotation of the connecting joints between each bone, so that when the upper limb is applied to each bone in the exoskeleton form of the training module 300, only the rotational angle and the rotational direction of the joint connection between each bone need to be controlled to realize the auxiliary movement of the upper limb.

Preferably, the step of calculating the driving angle and the driving moment of the training module 300 by the processing module 200 according to the distribution of the virtual upper limb and the target state in the virtual environment in the display module 400 mainly includes:

s210: obtaining the current position of the tail end of the virtual upper limb and the target position of the target state corresponding to the rehabilitation training task, and calculating the driving angle of each joint from the current position and the target position based on an inverse kinematics algorithm;

S220: on-line adjusting arm supporting moment of each joint of the training module 300 based on iterative learning, and calculating to obtain arm supporting moment required by each joint according to the target driving angle, the actual joint angle corresponding to the target driving angle, the target joint angular velocity and the actual joint angular velocity;

s230: performing inverse dynamics control based on feedback linearization, calculating to obtain output moment of each joint according to target motion parameters and actual motion parameters of each joint, and calculating to obtain driving quantity of each joint according to the output moment of each joint;

s240: and controlling the joint to be trained to apply driving moment according to the driving quantity of each joint so as to perform rehabilitation training.

Preferably, the training module 300 is based on the driving angle and the driving moment calculated by the processing module 200, wherein the calculated driving moment is used as the maximum driving moment which can be provided by the training module 300 under the auxiliary movement, so as to assist the upper limb of the cerebral apoplexy patient to move.

Preferably, the driving torque of the training module 300 is configured to be increased in a gradual and slow manner, and to stop the increase when the real upper limb has a movement tendency, and to assist in driving the real upper limb by a margin of the current driving torque plus a preset proportion of the current driving torque. For patients who do not completely paralyze, a better rehabilitation training mode is to enable the cerebral apoplexy patient to automatically drive the upper limb to move, while the technical scheme of the application is designed based on the mode that the driving moment applied by the training module 300 is increased from zero, under the condition that the real upper limb moves, the driving moment applied by the training module 300 is unchanged, only when the real upper limb does not move for a period of time, the driving moment of the training module 300 gradually starts to increase, the increasing speed is slower, and during the increasing process, if the movement trend of the real upper limb occurs, the driving moment of the training module 300 stops to increase, and a margin is increased according to the preset proportion (for example, one tenth) of the driving moment when the movement trend occurs, namely, under the mode, the driving force of the actual movement of the real upper limb is still driven mainly by the cerebral apoplexy patient, the training module 300 is driven in an auxiliary mode, the increased margin can avoid excessive use of muscles, and the muscle health of the cerebral apoplexy patient can be protected.

Compared with the existing driving scheme of completely providing driving force, the training module 300 of the application takes the exercise of the bilateral upper limbs of the cerebral apoplexy patient as an important point, and the cerebral apoplexy patient looks at the exercise state and the target state of the virtual upper limbs in the display module 400 during exercise, and the exercise mode of moving the virtual upper limbs to the target state is continuously imagined in the brain, so that the electroencephalogram signals are continuously stimulated to send driving signals to neurons connected with muscles, so that the muscles shrink as much as possible, and the training module 300 is utilized to assist the upper limbs to exercise under the condition that the muscles shrink to the maximum extent that the muscles can shrink currently, so that the brain can remember the feedback signals of the muscle exercise, and the control connection between the brain and the muscles of the upper limbs in the rehabilitation training is enhanced, thereby improving the efficiency of the rehabilitation training.

Example 3

The present embodiment is modified and supplemented based on the embodiments 1 and 2, and the repeated descriptions are omitted.

In practical situations, daily activities of living mostly need participation of double upper limbs every day, paralysis of one upper limb seriously affects a patient to engage in functional activities needing participation of double upper limbs, one upper limb activity has different nerve control mechanisms from the double upper limb activity, and the functional activities participated in by the double upper limbs can be obtained only through simultaneous training of the double upper limbs, for example, one upper limb fixing object, one upper limb operating object, such as screwing a bottle cap, cutting a vegetable, and the like. The simultaneous training of the two upper limbs can improve the functions of paralyzed upper limbs, and particularly, the upper limb proximal end functions are improved more beneficially, because the muscles of the trunk and the proximal ends of the limbs are bilaterally innervated, and bilaterally symmetric training can generate larger myoelectricity of the trunk muscles, so that the trunk is promoted to be stable, which is important for the control of the proximal ends of the limbs. During training, different upper limb operations need to be selected according to the specific conditions of patients.

Therefore, in the course of actual rehabilitation, the recovery variability of the bilateral upper limbs needs to be considered. If the auxiliary training is performed in the same manner as the bilateral driving moment, the recovery degree of the bilateral upper limbs may be different, and whether the difference occurs or not can be determined by the electromyographic signals acquired by the acquisition module 100, and when the auxiliary rehabilitation training is performed on the bilateral upper limbs by using the same driving moment under the condition of the difference, the difference value of the recovery degree between the bilateral upper limbs may be larger and larger, so that the uncoordinated situation between the bilateral upper limbs is caused.

Therefore, the present application proposes a rehabilitation evaluation system for upper limbs of a patient suffering from cerebral apoplexy as shown in fig. 1, which at least comprises an acquisition module 100, a processing module 200, a training module 300 and a display module 400, wherein the processing module 200 can calculate a driving angle and a driving moment which are required to be applied to the real upper limb by the training module 300 for assisting movement according to a target state in the display module 400 and a distance between virtual upper limbs, and the processing module 200 can adjust the driving moment applied to the two-sided upper limbs according to a difference value between a first myoelectric signal and a second myoelectric signal of the two-sided upper limbs acquired by the myoelectric acquisition part 110 of the acquisition module 100.

Specifically, assuming that the myoelectric signal of the left upper limb of the stroke patient acquired by the myoelectric acquisition unit 110 of the acquisition module 100 is denoted as a first myoelectric signal, the myoelectric signal of the right upper limb of the stroke patient acquired by the myoelectric acquisition unit 110 is denoted as a second myoelectric signal, the processing module 200 analyzes the first and second myoelectric signals, calculates a difference value between the first and second myoelectric signals, and adjusts the driving torque applied to the bilateral upper limb by the training module 300 according to the difference value.

Preferably, the analysis of the electromyographic signals by the processing module 200 mainly comprises original surface electromyographic signal analysis and processed data analysis, the data analysis mainly focuses on two aspects of time domain and frequency domain analysis, the purpose of the signal analysis mainly is to study the correlation between the time-frequency characteristics of the surface electromyographic signals and the muscle structure and the muscle activity state and the functional state, and to investigate the possible reasons of the surface signal changes, so that the changes of the electromyographic signals are effectively applied to reflect the muscle activity and the function. The surface myoelectric original signal is used as the most direct expression form for displaying the occurrence and the rest condition of myoelectric activity, and under the condition of not considering the amplitude, the starting relation of the myoelectric signals, namely the intensity and the height of the original myoelectric signals during the muscle activity, can be analyzed, and the amplitude and the strength of the muscle contraction can be reflected to a certain extent. The higher the density and height, the stronger the surface electromyographic signals, the stronger the shrinkage; the processed data analysis is to rectify, smooth and MVC normalize the directly recorded original surface electromyographic signals by using a signal processing system in software, and further calculate and analyze the original signals. For example, the analysis software Ergolab provides a data analysis mode after surface myoelectricity original signal analysis, time domain analysis, frequency domain analysis, segmentation analysis and the like.

Preferably, the processing module 200 is at least capable of analyzing the myoelectric signals acquired by the acquisition module 100 to determine a muscle contraction state, and determining whether the muscle movement is normal according to a preset muscle contraction threshold, specifically, the processing module 200 mainly monitors an abnormal muscle contraction state, determines whether the muscle is in an abnormal movement state by calculating a difference between a muscle contraction value obtained by the myoelectric signals acquired in real time and the preset abnormal muscle contraction threshold, and monitors an abnormal muscle contraction mode occurring in a rehabilitation training process. Wherein the muscle abnormal contraction pattern includes muscle hyperexcitability, muscle over-suppression, muscle abnormal synergy, active and antagonistic muscle abnormal co-contraction, and muscle fatigue.

Preferably, the processing module 200 is further capable of judging connection response conditions of different muscles and electroencephalogram signals in the exercise process based on the myoelectric signal sizes of different muscles of the same upper limb acquired by the myoelectric acquisition parts 100 of the acquisition module 100. Specifically, the upper limb includes, in order from the shoulder to the finger, deltoid, biceps brachii, triceps brachii, brachial, supinator, brachial radial, extensor radialis carpi longus, extensor radialis carpi brevis, extensor radialis flexor radialis, longus palmaris, extensor digitorum superficialis, extensor ulnar carpi ulnaris, etc., when the upper limb moves, the extension and contraction conditions of the muscles of each part are not uniform, the processing module 200 determines the electromyographic signal range in which the muscles of the upper limb should be located according to the current movement of the upper limb analyzed by the movement capturing part 120, and determines the vitality of each muscle in the rehabilitation process according to the myoelectric signals of different parts actually collected by the myoelectric collection part 110 of the collection module, for example, in the arm bending action, the biceps brachii, triceps brachii and brachii should be in a relatively active state, if the fact that the myoelectric signals of the triceps brachii are too weak is detected in the process, the processing module 200 considers that the connectivity of the triceps brachii and the brain is more, gives an evaluation of weaker vitality to the triceps brachii, and takes the exercise posture capable of training the triceps brachii as much as possible in the subsequent rehabilitation process so as to comprehensively improve the rehabilitation effect.

Preferably, the processing module 200 analyzes the difference value between the first electromyographic signal and the second electromyographic signal based on the above analysis manner, specifically, the processing module 200 evaluates the coordination of the bilateral upper limbs of the cerebral apoplexy patient based on the similarity of the first electromyographic signal and the second electromyographic signal, wherein the first electromyographic signal and the second electromyographic signal are electromyographic signals synchronously acquired under the condition that the bilateral upper limbs execute the same action.

Preferably, the processing module 200 is capable of evaluating bilateral upper limb rehabilitation coordination of the cerebral apoplexy patient based on a similarity value between the first electromyographic signal and the second electromyographic signal, and the processing module 200 is capable of classifying the bilateral upper limb coordination degree of the cerebral apoplexy patient into at least one, two, three, four, five, or more levels based on a similarity value between the first electromyographic signal and the second electromyographic signal, specifically, one of: statistically, the similarity value of the first electromyographic signal and the second electromyographic signal reaches 91-100%, which indicates that the coordination training effect of the patient is good in the rehabilitation training process; and (2) second-stage: statistically, the similarity value of the first electromyographic signal and the second electromyographic signal reaches 81-90%, which indicates that the coordination training effect of the patient is to be improved in the rehabilitation training process; three stages: statistically, the similarity value of the first electromyographic signal and the second electromyographic signal reaches 61% -80%, which indicates that the coordination training effect of the patient is poor in the rehabilitation training process; four stages: statistically, the similarity value of the first electromyographic signal and the second electromyographic signal reaches 41% -60%, which indicates that the coordination training effect of the patient is unqualified in the rehabilitation training process; five-stage, in statistical sense, the similarity value of the first electromyographic signal and the second electromyographic signal is less than or equal to 40%, which indicates that the coordination training effect of the patient is very bad in the rehabilitation training process.

In the foregoing calculation of the driving moment of the training module 300, the driving moment of the training module 300 is configured to be enhanced in a gradual and slow manner, and to stop enhancing when the real upper limb has a movement tendency, and to assist driving the real upper limb with a margin of the current driving moment plus a preset proportion of the current driving moment, in combination with a difference value between the first electromyographic signal and the second electromyographic signal, the calculation of the driving moment applied to the bilateral upper limb needs to be further improved, and the preset proportion for calculating the margin can be changed at least based on the magnitude of the difference value between the first electromyographic signal and the second electromyographic signal. With reference to the foregoing, the predetermined ratio refers to the ratio between the value of the margin and the current drive torque, and is typically expressed and calculated in terms of the margin being a fraction of the current drive torque.

Preferably, the processing module 200 adjusts the magnitude of the residual value in a manner of adjusting the preset ratio based on the difference value between the first electromyographic signal and the second electromyographic signal, and further adjusts the total value of the driving torque applied to one of the upper limbs.

Specifically, assuming that the intensity of the first electromyographic signal of the left upper limb is greater than the intensity of the second electromyographic signal of the right upper limb, that is, the recovery degree of the left upper limb is better than that of the right upper limb, therefore, when the training module 300 applies the driving moment, the driving moment applied to the right upper limb is smaller than that of the left upper limb, the design logic of the mode is that the lower driving moment is applied to the right upper limb with poor recovery degree, so that the right upper limb is exercised more, and the attention of a cerebral apoplexy patient is concentrated to drive the right upper limb to move during the movement of the right upper limb due to the lower driving moment, so that the recovery effect of the right upper limb is gradually leveled with the left upper limb, and the coordinated effect of the bilateral upper limb is realized during the rehabilitation training of the bilateral upper limb.

Preferably, the method of determining the preset ratio according to the difference value between the first electromyographic signal and the second electromyographic signal may be a stepwise corresponding method. For example, in the foregoing patient coordination training grading manner during rehabilitation training, the first level indicates that the preset ratio for calculating the residual value among the driving torques applied to the left upper limb and the right upper limb remains the same, the second level indicates that the preset ratio for calculating the residual value among the driving torques applied to the left upper limb assumes a default value (0.1, ten percent), and the preset ratio for calculating the residual value among the driving torques applied to the right upper limb may be set to 0.08, that is, eight percent; three-stage indicates that the preset ratio for calculating the residual value in the driving torque applied to the left upper limb assumes a default value (0.1, ten percent), and the preset ratio for calculating the residual value in the driving torque applied to the right upper limb may be set to 0.06, that is, six percent; the fourth stage indicates that the preset ratio for calculating the residual value in the driving torque applied to the left upper limb assumes a default value (0.1, ten percent), and the preset ratio for calculating the residual value in the driving torque applied to the right upper limb may be set to 0.04, that is, four percent; the fifth level indicates that the preset ratio for calculating the residual value in the driving torque applied to the left upper limb assumes a default value (0.1, ten percent), and the preset ratio for calculating the residual value in the driving torque applied to the right upper limb may be set to 0.02, that is, two percent.

Example 4

The present embodiment is modified and supplemented based on embodiments 1-3, and the repeated descriptions are omitted.

The application provides an upper limb rehabilitation evaluation system for a cerebral apoplexy patient, which at least comprises an acquisition module 100, a processing module 200, a training module 300 and a display module 400, wherein the training module 300 is approximately in an exoskeleton form, and the display module 400 is a regular display screen.

Preferably, the acquisition module 100 is mainly divided into an myoelectricity acquisition part 110 and a motion capture part 120, wherein the myoelectricity acquisition part 110 and the motion capture part 120 are separately configured, and the myoelectricity acquisition part 110 and the training module 300 are configured at the bilateral upper limbs of the cerebral apoplexy patient together, specifically, electrode pieces, arm rings, sensors and the like of the myoelectricity acquisition part 110 are clamped in the middle by the training module 300 and the upper limb skin of the cerebral apoplexy patient; the motion capture unit 120 of the acquisition module 100 is disposed in conjunction with the display module 400, and specifically, the image pickup device of the motion capture unit 120 is disposed on the display module 400.

Preferably, the processing module 200 can be integrated in the display screen of the display module 400 in a software form, and can be installed in the display module 400 in a chip form, the processing module 200 can at least receive information from the acquisition module 100, and construct a virtual upper limb in the screen (virtual environment) of the display module 400 after processing according to the collected action and posture information of the real upper limb, and the processing module 200 can also judge the coordination condition of the left and right upper limbs according to the collected electromyographic signals of the real upper limb, and output auxiliary motion parameters to be provided to the training module 300 in combination with the difference between the virtual upper limb and the target state and the coordination condition of the left and right upper limbs in the display module 400, wherein the auxiliary motion parameters mainly comprise a driving angle and a driving moment.

Preferably, fig. 3 shows a simplified schematic structure of a training module 300 of the upper limb rehabilitation evaluation system for a cerebral apoplexy patient according to the present application, wherein the training module 300 at least comprises a first exoskeleton, a second exoskeleton, a third exoskeleton and a fourth exoskeleton, wherein the first exoskeleton and the second exoskeleton are movably connected through a first external joint, the second exoskeleton and the third exoskeleton are movably connected through a second external joint, and the third exoskeleton and the fourth exoskeleton are movably connected through a third external joint. Specifically, the first exoskeleton is structurally designed according to the shape of the shoulder of the human body, and is used for being wrapped on the shoulder of the human body, the second exoskeleton is structurally designed according to the shape of the upper arm of the human body, and is used for being wrapped on the upper arm of the human body, the third exoskeleton is structurally designed according to the shape of the forearm of the human body, and is used for being wrapped on the forearm of the human body, the fourth exoskeleton is structurally designed according to the shape of the palm and the finger of the human body, and is used for being wrapped on the palm and the finger of the human body, wherein a first outer joint between the first exoskeleton and the second exoskeleton is similar to a shoulder joint of the human body, a second outer joint between the second exoskeleton and the third exoskeleton is similar to an elbow joint of the human body, and a third outer joint between the third exoskeleton and the fourth exoskeleton is similar to a wrist joint of the human body. In the prior art, the specific driving principle of the training module 300 can refer to the existing technical principle, the training module 300 of the present application is advantageous in that the driving moment provided by the training module 300 can be used for providing an auxiliary force only for the upper limb movement of the cerebral apoplexy patient slightly according to the control of the processing module 200, and not actively driving the upper limb to move, and the training module 300 of the present application can also be used for more training one of the upper limbs with poorer rehabilitation effect by adjusting the magnitude of the auxiliary driving moment at the left and right upper limbs based on the coordination between the left and right upper limbs analyzed by the processing module 200, so that the attention of the cerebral apoplexy patient is more focused on one of the upper limbs with poorer rehabilitation effect, and the difference of the rehabilitation degree between the two upper limbs is gradually reduced, so that the coordination of the two upper limbs is achieved.

Based on the scheme, the application focuses on the limb movement difference of the left side and the right side of the patient, provides different and targeted training parameters for the two sides of the patient, improves the training effect and the training efficiency, simultaneously avoids the condition that the left and the right limb muscles are uncoordinated in the rehabilitation process due to the application of unsuitable training intensity, solves the problem that the normal life of the patient is blocked by uncoordinated actions of the two sides of the limb, and has a certain positive influence on the life quality of the cerebral apoplexy patient.

It should be noted that the above-described embodiments are exemplary, and that a person skilled in the art, in light of the present disclosure, may devise various solutions that fall within the scope of the present disclosure and fall within the scope of the present disclosure. It should be understood by those skilled in the art that the present description and drawings are illustrative and not limiting to the claims. The scope of the application is defined by the claims and their equivalents.

Claims

1. An upper limb rehabilitation evaluation system for a cerebral apoplexy patient at least comprises:

an acquisition module (100) for acquiring upper limb information of a stroke patient;

A processing module (200) for processing upper limb information acquired by the acquisition module (100);

a training module (300) for assisting an upper limb movement of a stroke patient;

a display module (400) for displaying the upper limb movement state of a stroke patient;

it is characterized in that the method comprises the steps of,

the display module (400) displays the difference between the virtual upper limb and the target state to a cerebral apoplexy patient so that the cerebral apoplexy patient drives the real upper limb to move;

the processing module (200) judges the movement capacity of the real upper limb based on the difference change between the virtual upper limb and the target state, and controls the training module (300) to carry out auxiliary driving according to the movement capacity,

wherein the processing module (200) adjusts the driving moment applied to the bilateral upper limbs by the training module (300) based on the electromyographic signal difference between the bilateral upper limbs acquired by the acquisition module (100).

2. The upper limb rehabilitation evaluation system of the cerebral apoplexy patient according to claim 1, wherein the processing module (200) controls the driving moment of the training module (300) to be gradually increased, and the training module (300) stops to increase when the real upper limb has a movement trend, and performs auxiliary driving on the real upper limb by adding a margin value of a preset proportion of the current driving moment to the current driving moment.

3. The upper limb rehabilitation evaluation system of the cerebral apoplexy patient according to claim 1 or 2, wherein the processing module (200) adjusts the magnitude of the surplus value in a manner of adjusting a preset ratio based on the difference value between the first electromyographic signal and the second electromyographic signal of the bilateral upper limb respectively acquired by the acquisition module (100), and further adjusts the total value of the driving moment applied to one of the bilateral upper limbs.

4. A rehabilitation assessment system for upper limbs of a patient suffering from cerebral apoplexy according to any one of claims 1 to 3, wherein the processing module (200) is capable of constructing a virtual upper limb in a virtual environment of the display module (400) based on at least the electromyographic signals and the movement information of the real upper limb acquired by the acquisition module (100), wherein the acquisition module (100) comprises at least an electromyographic acquisition part (110) for acquiring electromyographic signals and a motion capture part (120) for capturing motion.

5. The upper limb rehabilitation assessment system of a stroke patient according to any one of claims 1-4, wherein the step of the processing module (200) mapping a real upper limb to a virtual upper limb of a virtual environment comprises:

building a model of a virtual upper limb in a virtual environment;

6. The upper limb rehabilitation evaluation system for a cerebral apoplexy patient according to any one of claims 1 to 5, wherein the display module (400) is configured in a manner that can be observed by a cerebral apoplexy patient, so that the cerebral apoplexy patient constantly looks like a situation in which the virtual upper limb moves to a target state when observing a picture of the virtual upper limb and the target state in the display module (400).

7. The upper limb rehabilitation evaluation system for the cerebral apoplexy patient according to any one of claims 1 to 6, wherein the processing module (200) can analyze the time and the movement state of the virtual upper limb in the display module (400) to the target state, and judge the movement capability of the real upper limb in the actual environment according to the time and the movement state.

8. The upper limb rehabilitation evaluation system of a cerebral apoplexy patient according to any one of claims 1-7, wherein the processing module (200) is capable of analyzing a driving angle and a driving moment required by the training module (300) for performing the auxiliary movement based on inverse kinematics, wherein the driving moment calculated by the processing module (200) is a maximum driving moment which can be provided by the training module (300) under the current auxiliary movement.

9. The upper limb rehabilitation evaluation system of a stroke patient according to any one of claims 1 to 8, wherein the step of the processing module (200) calculating the driving angle and the driving moment comprises:

10. The upper limb rehabilitation evaluation system for a cerebral apoplexy patient according to any one of claims 1 to 9, wherein the upper limb rehabilitation evaluation system for a cerebral apoplexy patient can be applied to upper limb rehabilitation effect evaluation of a cerebral apoplexy patient in individual families.